Many signals derived from real world systems exhibit relatively long-term, slow-changing trends. Sometimes a signal also includes cyclic patterns at different time scales. The trends and cyclic patterns are often hidden by faster changing noise or other signal artifacts. Traditional anomaly detection algorithms typically do not distinguish between long-term trends, cyclic patterns, and residual components. In particular, traditional anomaly detection algorithms often lead to false alarms and undetected anomalies because the long-term trends and/or cyclic patterns interfere with the algorithms. Interfering with anomaly detection algorithms often leads to false alarms and undetected anomalies.
Systems and methods to detect anomalies while accounting for long-term trends and cyclic patterns would therefore be of great benefit in data analysis.